Communication-Aware Control of Large Data Transmissions via Centralized Cognition and 5G Networks for Multi-Robot Map merging

Abstract

Multiple modern robotic applications benefit from centralized cognition and processing schemes. However, modern equipped robotic platforms can output a large amount of data, which may exceed the capabilities of modern wireless communication systems if all data is transmitted without further consideration. This research presents a multi-agent, centralized, and real-time 3D point cloud map merging scheme for ceaselessly connected robotic agents. Centralized architectures enable mission awareness to all agents at all times, making tasks such as search and rescue more effective. The centralized component is placed on an edge server, ensuring low communication latency, while all agents access the server utilizing a fifth-generation (5G) network. In addition, the proposed solution introduces a communication-aware control function that regulates the transmissions of map instances to prevent the creation of significant data congestion and communication latencies as well as address conditions where the robotic agents traverse in limited to no coverage areas. The presented framework is agnostic of the used localization and mapping procedure, while it utilizes the full power of an edge server. Finally, the efficiency of the novel established framework is being experimentally validated based on multiple scenarios.